{"ID":2864634,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23189","arxiv_id":"2509.23189","title":"AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms","abstract":"Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large Language Models (LLMs) as zero-shot reasoning engines for algorithm control. AutoEP's core innovation lies in a tight synergy between two components: (1) an online Exploratory Landscape Analysis (ELA) module that provides real-time, quantitative feedback on the search dynamics, and (2) a multi-LLM reasoning chain that interprets this feedback to generate adaptive hyperparameter strategies. This approach grounds high-level reasoning in empirical data, mitigating hallucination. Evaluated on three distinct metaheuristics across diverse combinatorial optimization benchmarks, AutoEP consistently outperforms state-of-the-art tuners, including neural evolution and other LLM-based methods. Notably, our framework enables open-source models like Qwen3-30B to match the performance of GPT-4, demonstrating a powerful and accessible new paradigm for automated hyperparameter design. Our code is available at https://github.com/YiZheZhang12/AutoEP.","short_abstract":"Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large La...","url_abs":"https://arxiv.org/abs/2509.23189","url_pdf":"https://arxiv.org/pdf/2509.23189v2","authors":"[\"Zhenxing Xu\",\"Yizhe Zhang\",\"Weidong Bao\",\"Hao Wang\",\"Ming Chen\",\"Haoran Ye\",\"Wenzheng Jiang\",\"Hui Yan\",\"Ji Wang\"]","published":"2025-09-27T08:45:21Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false,"code_links":[{"ID":609177,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864634,"paper_url":"https://arxiv.org/abs/2509.23189","paper_title":"AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms","repo_url":"https://github.com/YiZheZhang12/AutoEP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}